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Detection of ECG characteristic points using wavelet transforms

IEEE Transactions on Biomedical Engineering · 1995 · Vol. 42(1) · pp. 21–28
Cuiwei LiChong-Xun ZhengChangfeng Tai

Abstract

An algorithm based on wavelet transforms (WT's) has been developed for detecting ECG characteristic points. With the multiscale feature of WT's, the QRS complex can be distinguished from high P or T waves, noise, baseline drift, and artifacts. The relation between the characteristic points of ECG signal and those of modulus maximum pairs of its WT's is illustrated. By using this method, the detection rate of QRS complexes is above 99.8% for the MIT/BIH database and the P and T waves can also be detected, even with serious baseline drift and noise.

ECG Monitoring and AnalysisBlind Source Separation TechniquesEEG and Brain-Computer InterfacesQRS complexNoise (video)Wavelet transformPattern recognition (psychology)Artificial intelligenceWaveletFeature (linguistics)SIGNAL (programming language)Feature extractionLine (geometry)

MeSH terms

AlgorithmsArrhythmias, CardiacElectrocardiographyFourier AnalysisHumansSignal Processing, Computer-Assisted
Citations
1,595
FWCI
9.02
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References
16
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98%
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References
Characterization of signals from multiscale edges
IEEE Transactions on Pattern Analysis and Machine Intelligence · 1992 · 2,534 citations
Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database
IEEE Transactions on Biomedical Engineering · 1986 · 1,138 citations
Singularity detection and processing with wavelets
IEEE Transactions on Information Theory · 1992 · 3,854 citations
A Real-Time QRS Detection Algorithm
IEEE Transactions on Biomedical Engineering · 1985 · 7,635 citations
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